Overview

Dataset statistics

Number of variables11
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory278.3 KiB
Average record size in memory96.0 B

Variable types

Numeric11

Alerts

gross_revenue is highly overall correlated with invoice_no and 2 other fieldsHigh correlation
recency_days is highly overall correlated with invoice_noHigh correlation
invoice_no is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
quantity is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with avg_ticketHigh correlation
avg_ticket is highly skewed (γ1 = 53.4442279)Skewed
qt_returns is highly skewed (γ1 = 51.79774426)Skewed
avg_basket_size is highly skewed (γ1 = 44.68328098)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.1%) zerosZeros
qt_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-01-12 14:41:21.426630
Analysis finished2023-01-12 14:41:59.925613
Duration38.5 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:00.108556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2023-01-12T11:42:00.486231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.2261
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:00.793033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.491
Coefficient of variation (CV)3.8485342
Kurtosis353.95857
Mean2749.2261
Median Absolute Deviation (MAD)672.72
Skewness16.777879
Sum8162452.2
Variance1.1194678 × 108
MonotonicityNot monotonic
2023-01-12T11:42:01.179097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.96 2
 
0.1%
533.33 2
 
0.1%
889.93 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
331 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140438.72 1
< 0.1%
124564.53 1
< 0.1%
117375.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.288649
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:01.494689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756171
Coefficient of variation (CV)1.2094852
Kurtosis2.7780386
Mean64.288649
Median Absolute Deviation (MAD)26
Skewness1.7983969
Sum190873
Variance6046.0221
MonotonicityNot monotonic
2023-01-12T11:42:01.731595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
22 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

invoice_no
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7228023
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:01.989298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8566539
Coefficient of variation (CV)1.5476079
Kurtosis190.82536
Mean5.7228023
Median Absolute Deviation (MAD)2
Skewness10.766456
Sum16991
Variance78.440319
MonotonicityNot monotonic
2023-01-12T11:42:02.219082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 786
26.5%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 786
26.5%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

quantity
Real number (ℝ)

Distinct48
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.621421
Minimum1
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:02.454236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median11
Q314
95-th percentile22
Maximum102
Range101
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.2631661
Coefficient of variation (CV)0.53893288
Kurtosis25.435157
Mean11.621421
Median Absolute Deviation (MAD)3
Skewness3.1042989
Sum34504
Variance39.22725
MonotonicityNot monotonic
2023-01-12T11:42:02.806078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
10 288
 
9.7%
9 262
 
8.8%
11 255
 
8.6%
12 220
 
7.4%
8 217
 
7.3%
7 212
 
7.1%
13 199
 
6.7%
14 165
 
5.6%
6 157
 
5.3%
15 138
 
4.6%
Other values (38) 856
28.8%
ValueCountFrequency (%)
1 19
 
0.6%
2 32
 
1.1%
3 60
 
2.0%
4 82
 
2.8%
5 105
 
3.5%
6 157
5.3%
7 212
7.1%
8 217
7.3%
9 262
8.8%
10 288
9.7%
ValueCountFrequency (%)
102 1
 
< 0.1%
74 1
 
< 0.1%
58 2
0.1%
57 1
 
< 0.1%
56 1
 
< 0.1%
54 1
 
< 0.1%
50 2
0.1%
49 3
0.1%
44 4
0.1%
43 1
 
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.900057
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:03.037720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.974384
Q324.988286
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation1036.9343
Coefficient of variation (CV)19.979445
Kurtosis2890.7074
Mean51.900057
Median Absolute Deviation (MAD)5.9942223
Skewness53.444228
Sum154091.27
Variance1075232.8
MonotonicityNot monotonic
2023-01-12T11:42:03.262707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-67.35143
Minimum-366
Maximum-1
Zeros0
Zeros (%)0.0%
Negative2969
Negative (%)100.0%
Memory size46.4 KiB
2023-01-12T11:42:03.511888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-366
5-th percentile-201
Q1-85.333333
median-48.285714
Q3-25.928571
95-th percentile-8
Maximum-1
Range365
Interquartile range (IQR)59.404762

Descriptive statistics

Standard deviation63.542829
Coefficient of variation (CV)-0.94345182
Kurtosis4.8877032
Mean-67.35143
Median Absolute Deviation (MAD)26.285714
Skewness-2.062909
Sum-199966.4
Variance4037.6912
MonotonicityNot monotonic
2023-01-12T11:42:03.754721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14 25
 
0.8%
-4 22
 
0.7%
-70 21
 
0.7%
-7 20
 
0.7%
-35 19
 
0.6%
-49 18
 
0.6%
-21 17
 
0.6%
-46 17
 
0.6%
-11 17
 
0.6%
-1 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
-366 1
 
< 0.1%
-365 1
 
< 0.1%
-363 1
 
< 0.1%
-362 1
 
< 0.1%
-357 2
0.1%
-356 1
 
< 0.1%
-355 2
0.1%
-352 1
 
< 0.1%
-351 2
0.1%
-350 3
0.1%
ValueCountFrequency (%)
-1 16
0.5%
-1.5 1
 
< 0.1%
-2 13
0.4%
-2.5 1
 
< 0.1%
-2.601398601 1
 
< 0.1%
-3 15
0.5%
-3.321428571 1
 
< 0.1%
-3.330357143 1
 
< 0.1%
-3.5 2
 
0.1%
-4 22
0.7%

frequency
Real number (ℝ)

Distinct1350
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063271723
Minimum0.0054495913
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:04.014135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0094339623
Q10.017777778
median0.029411765
Q30.055401662
95-th percentile0.22222222
Maximum3
Range2.9945504
Interquartile range (IQR)0.037623884

Descriptive statistics

Standard deviation0.13448193
Coefficient of variation (CV)2.1254666
Kurtosis121.55969
Mean0.063271723
Median Absolute Deviation (MAD)0.014338235
Skewness8.7734265
Sum187.85375
Variance0.01808539
MonotonicityNot monotonic
2023-01-12T11:42:04.244449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1666666667 21
 
0.7%
0.3333333333 21
 
0.7%
0.02777777778 20
 
0.7%
0.09090909091 19
 
0.6%
0.0625 17
 
0.6%
0.1333333333 16
 
0.5%
0.4 16
 
0.5%
0.25 15
 
0.5%
0.02380952381 15
 
0.5%
0.03571428571 15
 
0.5%
Other values (1340) 2794
94.1%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005509641873 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
2 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.5 3
 
0.1%
1 14
0.5%
0.8333333333 1
 
< 0.1%
0.75 1
 
< 0.1%
0.6666666667 12
0.4%
0.6514745308 1
 
< 0.1%
0.6 1
 
< 0.1%

qt_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.156955
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:04.794576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.4961
Coefficient of variation (CV)24.333498
Kurtosis2765.5286
Mean62.156955
Median Absolute Deviation (MAD)1
Skewness51.797744
Sum184544
Variance2287644.6
MonotonicityNot monotonic
2023-01-12T11:42:05.095578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1973
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.34954
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:05.508490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172
Q3281.5
95-th percentile599.52
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation791.50241
Coefficient of variation (CV)3.1742686
Kurtosis2256.2455
Mean249.34954
Median Absolute Deviation (MAD)82.75
Skewness44.683281
Sum740318.79
Variance626476.07
MonotonicityNot monotonic
2023-01-12T11:42:05.868976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
86 9
 
0.3%
82 9
 
0.3%
136 8
 
0.3%
60 8
 
0.3%
75 8
 
0.3%
88 8
 
0.3%
71 7
 
0.2%
Other values (1963) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct1010
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.155074
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-12T11:42:06.129275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.513033
Coefficient of variation (CV)0.88074783
Kurtosis27.694698
Mean22.155074
Median Absolute Deviation (MAD)8.2
Skewness3.4982521
Sum65778.414
Variance380.75846
MonotonicityNot monotonic
2023-01-12T11:42:06.365059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 40
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 32
 
1.1%
18 31
 
1.0%
10 30
 
1.0%
16 29
 
1.0%
17 28
 
0.9%
Other values (1000) 2622
88.3%
ValueCountFrequency (%)
1 32
1.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

Interactions

2023-01-12T11:41:55.471967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:22.047012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:26.323948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:29.857257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:33.171083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:36.217244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:39.309245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:42.458261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:45.724545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:48.985162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:52.360844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:55.785744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:22.404352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:26.753585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:30.126202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:33.388535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:36.526713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:39.613770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:42.770563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:46.036409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:49.304626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:52.622849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:56.102304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:22.769895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:27.072218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:30.431602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:33.690651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:36.824106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:39.923295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:42.997687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:46.349463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:49.538651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:52.850748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:56.433471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:23.281246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:27.375354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:30.761178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:33.938359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:37.153413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:40.247676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:43.243902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:46.686470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:50.121539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:53.083725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:56.695876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:23.611079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:27.649789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:31.071279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:34.227774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:37.475853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:40.533624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:43.513504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:46.989309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:50.426633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:53.305140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:57.004964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:23.916206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:27.921591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:31.376806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:34.530235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:37.808619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:40.736905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:43.829744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:47.302105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:50.653015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:53.600446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:57.217475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:24.281923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:28.234493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:31.610275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:34.830558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:38.014282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:40.938949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:44.147485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:47.593316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:50.898873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:53.826554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:57.469654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:24.634374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:28.506823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:31.854524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:35.352634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:38.258692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:41.270193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:44.465648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:47.882019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:51.130138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:54.128957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:57.789082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:25.061637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:28.832307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:32.176863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:35.562137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:38.521919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:41.588983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:44.707946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:48.200552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:51.353655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:54.468158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:58.129042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:25.434008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:29.165003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:32.515745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:35.787544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:38.835160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:41.822368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:45.052267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:48.433435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:51.692828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:54.809109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:58.446038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:25.838266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:29.511247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:32.896262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:36.004908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:39.065260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:42.150281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:45.401247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:48.667515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:52.030768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T11:41:55.142064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-12T11:42:06.586801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysinvoice_noquantityavg_ticketavg_recency_daysfrequencyqt_returnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0760.0010.025-0.007-0.131-0.019-0.008-0.063-0.123-0.007
gross_revenue-0.0761.000-0.4150.7700.7700.2460.2480.1610.3720.5760.291
recency_days0.001-0.4151.000-0.502-0.3990.048-0.108-0.031-0.119-0.098-0.106
invoice_no0.0250.770-0.5021.0000.6600.0590.2580.1480.2930.1010.025
quantity-0.0070.770-0.3990.6601.000-0.0760.1780.1090.2730.5190.446
avg_ticket-0.1310.2460.0480.059-0.0761.0000.1220.0980.1900.189-0.611
avg_recency_days-0.0190.248-0.1080.2580.1780.1221.0000.9620.3960.077-0.048
frequency-0.0080.161-0.0310.1480.1090.0980.9621.0000.3590.057-0.042
qt_returns-0.0630.372-0.1190.2930.2730.1900.3960.3591.0000.2110.019
avg_basket_size-0.1230.576-0.0980.1010.5190.1890.0770.0570.2111.0000.448
avg_unique_basket_size-0.0070.291-0.1060.0250.446-0.611-0.048-0.0420.0190.4481.000

Missing values

2023-01-12T11:41:58.972394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-12T11:41:59.618978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysinvoice_noquantityavg_ticketavg_recency_daysfrequencyqt_returnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.06.018.152222-35.5000000.48611140.050.9705888.735294
1130473232.5956.09.011.018.904035-27.2500000.04878035.0154.44444419.000000
2125836705.382.015.024.028.902500-23.1875000.04569950.0335.20000015.466667
313748948.2595.05.08.033.866071-92.6666670.0179210.087.8000005.600000
415100876.00333.03.02.0292.000000-8.6000000.13636422.026.6666671.000000
5152914623.3025.014.017.045.326471-23.2000000.05444129.0150.1428577.285714
6146885630.877.021.024.017.219786-18.3000000.073569399.0172.42857115.571429
7178095411.9116.012.023.088.719836-35.7000000.03910641.0171.4166675.083333
81531160767.900.091.043.025.543464-4.1444440.315508474.0419.71428626.142857
9160982005.6387.07.015.029.934776-47.6666670.0243900.087.5714299.571429
customer_idgross_revenuerecency_daysinvoice_noquantityavg_ticketavg_recency_daysfrequencyqt_returnsavg_basket_sizeavg_unique_basket_size
5627177271060.2515.01.011.016.064394-6.00.2857146.0645.00000066.0
563717232421.522.02.010.011.708889-12.00.1538460.0101.50000018.0
563817468137.0010.02.02.027.400000-4.00.4000000.058.0000002.5
564913596697.045.02.010.04.199036-7.00.2500000.0203.00000083.0
5655148931237.859.02.014.016.956849-2.00.6666670.0399.50000036.5
565912479473.2011.01.08.015.773333-4.00.33333334.0382.00000030.0
568014126706.137.03.06.047.075333-3.01.00000050.0169.3333335.0
5686135211092.391.03.09.02.511241-4.50.3000000.0244.333333145.0
569615060301.848.04.08.02.515333-1.02.0000000.065.50000030.0
571512558269.967.01.05.024.541818-6.00.285714196.0196.00000011.0